Recent studies have clearly demonstrated an
increasing shift towards team science potentially
attributed to growing interdisciplinary,
multidisciplinary and transdisciplinary research.
Grants from federal agencies (e.g. Clinical
Translational Science Awards, NCATS) have
especially emphasized the importance of
translational research that in turn demand team
science approaches. Our recent studies have
successfully used network analytics of grant
collaborations to objectively quantify and assess
team science in translational settings in an
evidence-based/data-driven manner (Nagarajan
R et al., J. Biomedical Informatics 2013; Nagarajan
R et al. Clinical Translational Science 2015).
Collaborative grants are often the culminating
point or outcome of successful and sustained
research collaborations. Grant collaboration
data sets are accessible and curated diligently
with minimal errors making them a useful
resource for investigating team science efforts.
Grant collaboration networks (GCNs) provide a convenient abstraction of collaborations that
can be studied in a controlled and cost-effective
manner in-silico. In this presentation we show
that GCNs can provide insights into inherent nontrivial
community structures, cross-talk between
communities and their temporal evolution. The
strength of these communities as a function
of time is also investigated by using synthetic
surrogate network models (e.g. random graphs)
as internal controls. Understanding the temporal
evolution of these communities and their
deviation from random graphs can especially
be useful in evaluation in pre-/post-intervention
settings and has the potential to serve as
evaluation metrics. Inherent community structures
and cross-talk between communities in the GCN
can also assist in targeted resource allocation
that can impact policy. However, GCNs are opensystems
and prone to external perturbations and
confounders that demand careful interpretation
of the results. Forecasting of the GCNs can also be
challenging as the nodes as well as the edges are
not conserved as a function of time. Universality of
the findings presented will demand repeating the
exercise across diverse settings.